library(foreign)
library(tidyr)
library(ggplot2)
library(dplyr)
library(lubridate)
library(readr)
library(plotly)
library(scales)
# Theming
quartzFonts(
Roboto =
c("Roboto-Light",
"Roboto-Bold",
"Roboto-Regular",
"Roboto-Thin")
)
theme_set(
theme_bw(base_family = "Roboto", base_size = 10) +
theme(
plot.title = element_text(size = 14,
margin = margin(0, 0, 4, 0, "pt")),
plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 6),
plot.background = element_rect("#fafafa", "#fafafa"),
panel.background = element_rect("#fafafa"),
panel.border = element_blank()
)
)
rm(list=ls())load(file = "output/mediatenor.Rda")p <- df.reduced %>%
filter(category == "daily_print") %>%
filter(medium != "Berliner") %>%
ggplot(aes(year, wertung, color=p_group, group=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Tageszeitungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90),
legend.position = "bottom") +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
#p
ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "daily_print") %>%
filter(medium != "Berliner") %>%
ggplot(aes(year, weighted, color=p_group, group=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Tageszeitungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90),
legend.position = "bottom") +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "magazine_print") %>%
ggplot(aes(year, wertung, color=p_group, group = p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 5) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Magazine und Wochenzeitungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90),
legend.position = "bottom") +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "magazine_print") %>%
ggplot(aes(year, weighted, color=p_group, group = p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 5) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Magazine und Wochenzeitungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "news_tv") %>%
ggplot(aes(year, wertung, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Nachrichtensendungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "news_tv") %>%
ggplot(aes(year, weighted, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Nachrichtensendungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "polit_tv") %>%
ggplot(aes(year, wertung, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 6) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Politische TV-Shows (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90),
legend.position = "none") +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "polit_tv") %>%
ggplot(aes(year, weighted, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 6) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Politische TV-Shows (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
ggplotly(p, tooltop=c("medium", "weighted"))library(radarchart)radar <- df.reduced %>%
filter(category == "daily_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = wertung)chartJSRadar(scores = radar, labelSize = 12,
main = "Tageszeitungen (ungewichtet)",
scaleStartValue = -0.2,
maxScale = 0,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "daily_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = weighted)chartJSRadar(scores = radar, labelSize = 12,
main = "Tageszeitungen (gewichtet)",
scaleStartValue = -0.05,
maxScale = 0,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "magazine_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = wertung)chartJSRadar(scores = radar, labelSize = 12,
main = "Magazine und Wochenzeitungen (ungewichtet)",
scaleStartValue = -0.16,
maxScale = 0.02,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "magazine_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = weighted)chartJSRadar(scores = radar, labelSize = 12,
main = "Magazine und Wochenzeitungen (gewichtet)",
scaleStartValue = -0.05,
maxScale = 0.01,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "news_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = wertung)chartJSRadar(scores = radar, labelSize = 12,
main = "Nachritensendungen (ungewichtet)",
scaleStartValue = -0.1,
maxScale = -0.01,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "news_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = weighted)chartJSRadar(scores = radar, labelSize = 12,
main = "Nachritensendungen (gewichtet)",
scaleStartValue = -0.04,
maxScale = 0,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "polit_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = wertung)chartJSRadar(scores = radar, labelSize = 12,
main = "Politische TV-Shows (ungewichtet)",
scaleStartValue = -0.45,
maxScale = 0.1,
responsive = T,
showToolTipLabel = TRUE)radar <- df.reduced %>%
filter(category == "polit_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=medium, value = weighted)chartJSRadar(scores = radar, labelSize = 12,
main = "Politische TV-Shows (gewichtet)",
scaleStartValue = -0.16,
maxScale = 0.01,
responsive = T,
showToolTipLabel = TRUE)